deusdata--codebase-memory-mcp
wehub-resource-sync
40 行
1.5 KiB
C
40 行
1.5 KiB
C
/* nomic-embed-code (nomic-ai/nomic-embed-code) token embeddings.
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* 40856 tokens x 768d int8-quantized unit vectors.
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* Distilled from 7B model via full inference on filtered vocabulary.
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* Simulated attention: 3 iterations, K=32, alpha=0.3.
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*
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* Vector blob embedded via code_vectors_blob.S (assembler .incbin).
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* Token strings are in this header as a static array.
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*
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* Storage format: int8 × 127. We also tested float32 storage — it did NOT
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* improve performance because cooccur passes are memory-bandwidth-bound.
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* Float32 dense reads are 4x larger than int8, which cancels the CPU savings
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* from avoided int8→float conversion. int8 is a strict win on binary size
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* (30 MB vs 120 MB) and equal on runtime.
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*
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* Source: https://huggingface.co/nomic-ai/nomic-embed-code
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* License: Apache 2.0
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*/
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#ifndef CBM_NOMIC_VECTORS_H
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#define CBM_NOMIC_VECTORS_H
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#include <stdint.h>
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#define PRETRAINED_TOKEN_COUNT 40856
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#define PRETRAINED_DIM 768
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/* Raw vector blob: first 8 bytes = [int32 count][int32 dim],
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* then count x dim int8 values (unit-normalized, x127 scaled). */
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extern const unsigned char PRETRAINED_VECTOR_BLOB[];
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extern const unsigned int PRETRAINED_VECTOR_BLOB_LEN;
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/* Access the int8 vector for token index i. Zero-copy pointer into blob. */
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static inline const int8_t *pretrained_vec_at(int i) {
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return (const int8_t *)(PRETRAINED_VECTOR_BLOB + 8 + (size_t)i * PRETRAINED_DIM);
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}
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/* Token strings (separate header to keep this file clean). */
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#include "code_tokens.h"
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#endif /* CBM_NOMIC_VECTORS_H */
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